- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
1 Online-Ressource.
- Sprache
-
Englisch
- Erschienen in
-
Community detection in hypergraphs via mutual information maximization ; volume:14 ; number:1 ; day:23 ; month:3 ; year:2024 ; pages:1-18 ; date:12.2024
Scientific reports ; 14, Heft 1 (23.3.2024), 1-18, 12.2024
- Urheber
-
Kritschgau, Jürgen
Kaiser, Daniel
Alvarado Rodriguez, Oliver
Amburg, Ilya
Bolkema, Jessalyn
Grubb, Thomas
Lan, Fangfei
Maleki, Sepideh
Chodrow, Phil
Kay, Bill
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1038/s41598-024-55934-5
- URN
-
urn:nbn:de:101:1-2405270938270.820149006947
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:49 MESZ
Datenpartner
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Beteiligte
- Kritschgau, Jürgen
- Kaiser, Daniel
- Alvarado Rodriguez, Oliver
- Amburg, Ilya
- Bolkema, Jessalyn
- Grubb, Thomas
- Lan, Fangfei
- Maleki, Sepideh
- Chodrow, Phil
- Kay, Bill
- SpringerLink (Online service)
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Online continual learning through unsupervised mutual information maximization
Evaluating the role of community detection in improving influence maximization heuristics
Consistency-constrained RGB-T crowd counting via mutual information maximization
Blocks of hypergraphs : applied to hypergraphs and outerplanarity
Quasi-random hypergraphs and extremal problems for hypergraphs
Traces of Hypergraphs
Quasirandomness in hypergraphs
Drawing layered hypergraphs
Interaction on hypergraphs
Partitioning billionscale hypergraphs